Overview

Dataset statistics

Number of variables15
Number of observations2899
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory328.5 KiB
Average record size in memory116.0 B

Variable types

Numeric13
Categorical2

Warnings

日期 has a high cardinality: 2899 distinct values High cardinality
df_index is highly correlated with 总市值 and 1 other fieldsHigh correlation
收盘价 is highly correlated with 最高价 and 7 other fieldsHigh correlation
最高价 is highly correlated with 收盘价 and 7 other fieldsHigh correlation
最低价 is highly correlated with 收盘价 and 6 other fieldsHigh correlation
开盘价 is highly correlated with 收盘价 and 7 other fieldsHigh correlation
前收盘 is highly correlated with 收盘价 and 7 other fieldsHigh correlation
换手率 is highly correlated with 成交量 and 1 other fieldsHigh correlation
成交量 is highly correlated with 收盘价 and 7 other fieldsHigh correlation
成交金额 is highly correlated with 收盘价 and 8 other fieldsHigh correlation
总市值 is highly correlated with df_index and 8 other fieldsHigh correlation
流通市值 is highly correlated with df_index and 8 other fieldsHigh correlation
涨跌额 is highly correlated with 涨跌幅High correlation
涨跌幅 is highly correlated with 涨跌额High correlation
df_index is highly correlated with 总市值 and 1 other fieldsHigh correlation
收盘价 is highly correlated with 最高价 and 6 other fieldsHigh correlation
最高价 is highly correlated with 收盘价 and 6 other fieldsHigh correlation
最低价 is highly correlated with 收盘价 and 6 other fieldsHigh correlation
开盘价 is highly correlated with 收盘价 and 6 other fieldsHigh correlation
前收盘 is highly correlated with 收盘价 and 6 other fieldsHigh correlation
换手率 is highly correlated with 成交量 and 1 other fieldsHigh correlation
成交量 is highly correlated with 换手率 and 3 other fieldsHigh correlation
成交金额 is highly correlated with 收盘价 and 8 other fieldsHigh correlation
总市值 is highly correlated with df_index and 8 other fieldsHigh correlation
流通市值 is highly correlated with df_index and 8 other fieldsHigh correlation
涨跌额 is highly correlated with 涨跌幅High correlation
涨跌幅 is highly correlated with 涨跌额High correlation
df_index is highly correlated with 流通市值High correlation
收盘价 is highly correlated with 最高价 and 7 other fieldsHigh correlation
最高价 is highly correlated with 收盘价 and 7 other fieldsHigh correlation
最低价 is highly correlated with 收盘价 and 7 other fieldsHigh correlation
开盘价 is highly correlated with 收盘价 and 7 other fieldsHigh correlation
前收盘 is highly correlated with 收盘价 and 7 other fieldsHigh correlation
换手率 is highly correlated with 成交量 and 1 other fieldsHigh correlation
成交量 is highly correlated with 换手率 and 2 other fieldsHigh correlation
成交金额 is highly correlated with 收盘价 and 8 other fieldsHigh correlation
总市值 is highly correlated with 收盘价 and 7 other fieldsHigh correlation
流通市值 is highly correlated with df_index and 8 other fieldsHigh correlation
涨跌额 is highly correlated with 涨跌幅 and 1 other fieldsHigh correlation
涨跌幅 is highly correlated with 涨跌额 and 1 other fieldsHigh correlation
次日涨跌 is highly correlated with 收盘价 and 11 other fieldsHigh correlation
总市值 is highly correlated with 开盘价 and 9 other fieldsHigh correlation
开盘价 is highly correlated with 总市值 and 9 other fieldsHigh correlation
涨跌幅 is highly correlated with 涨跌额High correlation
成交量 is highly correlated with 总市值 and 10 other fieldsHigh correlation
最高价 is highly correlated with 总市值 and 9 other fieldsHigh correlation
成交金额 is highly correlated with 总市值 and 10 other fieldsHigh correlation
最低价 is highly correlated with 总市值 and 9 other fieldsHigh correlation
流通市值 is highly correlated with 总市值 and 9 other fieldsHigh correlation
收盘价 is highly correlated with 总市值 and 9 other fieldsHigh correlation
df_index is highly correlated with 总市值 and 8 other fieldsHigh correlation
换手率 is highly correlated with 成交量 and 1 other fieldsHigh correlation
涨跌额 is highly correlated with 总市值 and 9 other fieldsHigh correlation
前收盘 is highly correlated with 总市值 and 9 other fieldsHigh correlation
日期 is uniformly distributed Uniform
df_index has unique values Unique
日期 has unique values Unique
成交量 has unique values Unique
成交金额 has unique values Unique
涨跌额 has 69 (2.4%) zeros Zeros
涨跌幅 has 69 (2.4%) zeros Zeros

Reproduction

Analysis started2021-08-06 05:24:44.231063
Analysis finished2021-08-06 05:24:59.825685
Duration15.59 seconds
Software versionpandas-profiling v3.0.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct2899
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1590.219041
Minimum0
Maximum3212
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size22.8 KiB
2021-08-06T13:24:59.900417image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile144.9
Q1728.5
median1568
Q32475.5
95-th percentile3063.1
Maximum3212
Range3212
Interquartile range (IQR)1747

Descriptive statistics

Standard deviation967.1750962
Coefficient of variation (CV)0.608202437
Kurtosis-1.330087003
Mean1590.219041
Median Absolute Deviation (MAD)874
Skewness0.03818590785
Sum4610045
Variance935427.6666
MonotonicityStrictly increasing
2021-08-06T13:25:00.146719image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
22241
 
< 0.1%
22261
 
< 0.1%
22271
 
< 0.1%
22281
 
< 0.1%
22291
 
< 0.1%
22301
 
< 0.1%
22311
 
< 0.1%
22321
 
< 0.1%
22331
 
< 0.1%
Other values (2889)2889
99.7%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
32121
< 0.1%
32111
< 0.1%
32101
< 0.1%
32091
< 0.1%
32081
< 0.1%
32071
< 0.1%
32061
< 0.1%
32051
< 0.1%
32041
< 0.1%
32031
< 0.1%

日期
Categorical

HIGH CARDINALITY
UNIFORM
UNIQUE

Distinct2899
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size22.8 KiB
2021/8/5
 
1
2012/6/15
 
1
2012/6/13
 
1
2012/6/12
 
1
2012/6/11
 
1
Other values (2894)
2894 

Length

Max length10
Median length9
Mean length8.962400828
Min length8

Characters and Unicode

Total characters25982
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2899 ?
Unique (%)100.0%

Sample

1st row2021/8/5
2nd row2021/8/4
3rd row2021/8/3
4th row2021/8/2
5th row2021/7/30

Common Values

ValueCountFrequency (%)
2021/8/51
 
< 0.1%
2012/6/151
 
< 0.1%
2012/6/131
 
< 0.1%
2012/6/121
 
< 0.1%
2012/6/111
 
< 0.1%
2012/6/81
 
< 0.1%
2012/6/71
 
< 0.1%
2012/6/61
 
< 0.1%
2012/6/51
 
< 0.1%
2012/6/41
 
< 0.1%
Other values (2889)2889
99.7%

Length

2021-08-06T13:25:00.325306image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2021/8/51
 
< 0.1%
2012/6/151
 
< 0.1%
2012/6/131
 
< 0.1%
2012/6/121
 
< 0.1%
2012/6/111
 
< 0.1%
2012/6/81
 
< 0.1%
2012/6/71
 
< 0.1%
2012/6/61
 
< 0.1%
2012/6/51
 
< 0.1%
2012/6/41
 
< 0.1%
Other values (2889)2889
99.7%

Most occurring characters

ValueCountFrequency (%)
/5798
22.3%
25269
20.3%
14970
19.1%
04241
16.3%
9988
 
3.8%
8928
 
3.6%
7804
 
3.1%
3787
 
3.0%
6772
 
3.0%
4768
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number20184
77.7%
Other Punctuation5798
 
22.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
25269
26.1%
14970
24.6%
04241
21.0%
9988
 
4.9%
8928
 
4.6%
7804
 
4.0%
3787
 
3.9%
6772
 
3.8%
4768
 
3.8%
5657
 
3.3%
Other Punctuation
ValueCountFrequency (%)
/5798
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common25982
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
/5798
22.3%
25269
20.3%
14970
19.1%
04241
16.3%
9988
 
3.8%
8928
 
3.6%
7804
 
3.1%
3787
 
3.0%
6772
 
3.0%
4768
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII25982
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/5798
22.3%
25269
20.3%
14970
19.1%
04241
16.3%
9988
 
3.8%
8928
 
3.6%
7804
 
3.1%
3787
 
3.0%
6772
 
3.0%
4768
 
3.0%

收盘价
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1433
Distinct (%)49.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.25770956
Minimum3.57
Maximum51.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.8 KiB
2021-08-06T13:25:00.410269image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum3.57
5-th percentile4.159
Q18.09
median10.77
Q314.67
95-th percentile25.635
Maximum51.2
Range47.63
Interquartile range (IQR)6.58

Descriptive statistics

Standard deviation6.650284717
Coefficient of variation (CV)0.5425389374
Kurtosis4.052231069
Mean12.25770956
Median Absolute Deviation (MAD)3
Skewness1.619935896
Sum35535.1
Variance44.22628682
MonotonicityNot monotonic
2021-08-06T13:25:00.502353image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.413
 
0.4%
89
 
0.3%
8.979
 
0.3%
8.68
 
0.3%
8.418
 
0.3%
11.148
 
0.3%
4.048
 
0.3%
11.27
 
0.2%
12.077
 
0.2%
8.827
 
0.2%
Other values (1423)2815
97.1%
ValueCountFrequency (%)
3.571
 
< 0.1%
3.593
0.1%
3.61
 
< 0.1%
3.681
 
< 0.1%
3.72
0.1%
3.741
 
< 0.1%
3.751
 
< 0.1%
3.772
0.1%
3.781
 
< 0.1%
3.793
0.1%
ValueCountFrequency (%)
51.21
< 0.1%
49.531
< 0.1%
49.51
< 0.1%
49.41
< 0.1%
48.31
< 0.1%
48.081
< 0.1%
46.651
< 0.1%
46.61
< 0.1%
46.561
< 0.1%
46.151
< 0.1%

最高价
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1435
Distinct (%)49.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.5584098
Minimum3.61
Maximum53.13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.8 KiB
2021-08-06T13:25:00.586378image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum3.61
5-th percentile4.22
Q18.28
median10.95
Q314.99
95-th percentile26.131
Maximum53.13
Range49.52
Interquartile range (IQR)6.71

Descriptive statistics

Standard deviation6.879166209
Coefficient of variation (CV)0.5477736688
Kurtosis4.44559435
Mean12.5584098
Median Absolute Deviation (MAD)3.06
Skewness1.674542528
Sum36406.83
Variance47.32292773
MonotonicityNot monotonic
2021-08-06T13:25:00.675322image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.5513
 
0.4%
4.0912
 
0.4%
12.0910
 
0.3%
9.339
 
0.3%
8.558
 
0.3%
4.088
 
0.3%
9.788
 
0.3%
11.158
 
0.3%
12.28
 
0.3%
4.197
 
0.2%
Other values (1425)2808
96.9%
ValueCountFrequency (%)
3.611
< 0.1%
3.621
< 0.1%
3.661
< 0.1%
3.691
< 0.1%
3.731
< 0.1%
3.741
< 0.1%
3.772
0.1%
3.822
0.1%
3.831
< 0.1%
3.841
< 0.1%
ValueCountFrequency (%)
53.131
< 0.1%
52.981
< 0.1%
52.71
< 0.1%
51.891
< 0.1%
50.661
< 0.1%
49.981
< 0.1%
49.731
< 0.1%
49.51
< 0.1%
48.881
< 0.1%
48.861
< 0.1%

最低价
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1375
Distinct (%)47.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.94749569
Minimum3.48
Maximum49.84
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.8 KiB
2021-08-06T13:25:00.759699image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum3.48
5-th percentile4.08
Q17.9
median10.52
Q314.33
95-th percentile24.81
Maximum49.84
Range46.36
Interquartile range (IQR)6.43

Descriptive statistics

Standard deviation6.407963252
Coefficient of variation (CV)0.5363436338
Kurtosis3.722209969
Mean11.94749569
Median Absolute Deviation (MAD)2.92
Skewness1.566630021
Sum34635.79
Variance41.06199304
MonotonicityNot monotonic
2021-08-06T13:25:00.848284image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.811
 
0.4%
11.3810
 
0.3%
8.819
 
0.3%
8.99
 
0.3%
78
 
0.3%
11.58
 
0.3%
3.868
 
0.3%
7.888
 
0.3%
8.28
 
0.3%
7.88
 
0.3%
Other values (1365)2812
97.0%
ValueCountFrequency (%)
3.482
0.1%
3.51
 
< 0.1%
3.541
 
< 0.1%
3.551
 
< 0.1%
3.561
 
< 0.1%
3.611
 
< 0.1%
3.642
0.1%
3.671
 
< 0.1%
3.71
 
< 0.1%
3.713
0.1%
ValueCountFrequency (%)
49.841
< 0.1%
47.921
< 0.1%
47.31
< 0.1%
46.611
< 0.1%
45.721
< 0.1%
45.71
< 0.1%
451
< 0.1%
44.291
< 0.1%
43.471
< 0.1%
43.281
< 0.1%

开盘价
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1394
Distinct (%)48.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.22971369
Minimum3.53
Maximum50.25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.8 KiB
2021-08-06T13:25:00.932658image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum3.53
5-th percentile4.16
Q18.05
median10.7
Q314.665
95-th percentile25.482
Maximum50.25
Range46.72
Interquartile range (IQR)6.615

Descriptive statistics

Standard deviation6.616875003
Coefficient of variation (CV)0.5410490522
Kurtosis4.019029525
Mean12.22971369
Median Absolute Deviation (MAD)3
Skewness1.610315584
Sum35453.94
Variance43.7830348
MonotonicityNot monotonic
2021-08-06T13:25:01.023887image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1214
 
0.5%
8.311
 
0.4%
11.210
 
0.3%
9.310
 
0.3%
4.0310
 
0.3%
8.339
 
0.3%
9.19
 
0.3%
89
 
0.3%
10.78
 
0.3%
8.68
 
0.3%
Other values (1384)2801
96.6%
ValueCountFrequency (%)
3.531
 
< 0.1%
3.541
 
< 0.1%
3.572
0.1%
3.61
 
< 0.1%
3.651
 
< 0.1%
3.671
 
< 0.1%
3.711
 
< 0.1%
3.731
 
< 0.1%
3.742
0.1%
3.763
0.1%
ValueCountFrequency (%)
50.251
< 0.1%
501
< 0.1%
49.11
< 0.1%
49.011
< 0.1%
491
< 0.1%
48.51
< 0.1%
47.561
< 0.1%
45.921
< 0.1%
45.881
< 0.1%
45.541
< 0.1%

前收盘
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1435
Distinct (%)49.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.2493791
Minimum3.57
Maximum51.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.8 KiB
2021-08-06T13:25:01.111632image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum3.57
5-th percentile4.159
Q18.08
median10.76
Q314.67
95-th percentile25.567
Maximum51.2
Range47.63
Interquartile range (IQR)6.59

Descriptive statistics

Standard deviation6.62939826
Coefficient of variation (CV)0.5412027995
Kurtosis4.003919515
Mean12.2493791
Median Absolute Deviation (MAD)3
Skewness1.607949074
Sum35510.95
Variance43.94892128
MonotonicityNot monotonic
2021-08-06T13:25:01.199867image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8.413
 
0.4%
810
 
0.3%
4.048
 
0.3%
8.978
 
0.3%
8.418
 
0.3%
11.148
 
0.3%
8.68
 
0.3%
8.797
 
0.2%
11.627
 
0.2%
10.037
 
0.2%
Other values (1425)2815
97.1%
ValueCountFrequency (%)
3.571
 
< 0.1%
3.593
0.1%
3.61
 
< 0.1%
3.681
 
< 0.1%
3.72
0.1%
3.741
 
< 0.1%
3.751
 
< 0.1%
3.772
0.1%
3.781
 
< 0.1%
3.793
0.1%
ValueCountFrequency (%)
51.21
< 0.1%
49.531
< 0.1%
49.51
< 0.1%
49.41
< 0.1%
48.31
< 0.1%
48.081
< 0.1%
46.651
< 0.1%
46.61
< 0.1%
46.561
< 0.1%
46.151
< 0.1%

换手率
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2841
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.43753377
Minimum0.0564
Maximum30.9481
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.8 KiB
2021-08-06T13:25:01.290031image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum0.0564
5-th percentile0.52889
Q11.23805
median2.483
Q34.6203
95-th percentile9.48531
Maximum30.9481
Range30.8917
Interquartile range (IQR)3.38225

Descriptive statistics

Standard deviation3.088831522
Coefficient of variation (CV)0.8985603426
Kurtosis7.477158521
Mean3.43753377
Median Absolute Deviation (MAD)1.4839
Skewness2.105149554
Sum9965.4104
Variance9.540880173
MonotonicityNot monotonic
2021-08-06T13:25:01.377790image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.72553
 
0.1%
0.88723
 
0.1%
2.04262
 
0.1%
0.74712
 
0.1%
1.15422
 
0.1%
3.21022
 
0.1%
7.05362
 
0.1%
2.12732
 
0.1%
3.91132
 
0.1%
3.39842
 
0.1%
Other values (2831)2877
99.2%
ValueCountFrequency (%)
0.05641
< 0.1%
0.06951
< 0.1%
0.09641
< 0.1%
0.13981
< 0.1%
0.19981
< 0.1%
0.211
< 0.1%
0.21731
< 0.1%
0.23871
< 0.1%
0.24331
< 0.1%
0.24561
< 0.1%
ValueCountFrequency (%)
30.94811
< 0.1%
29.04611
< 0.1%
22.07911
< 0.1%
21.01611
< 0.1%
19.77241
< 0.1%
19.65651
< 0.1%
19.47091
< 0.1%
19.11791
< 0.1%
18.95441
< 0.1%
18.80511
< 0.1%

成交量
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct2899
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13187923.01
Minimum276620
Maximum131994091
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.8 KiB
2021-08-06T13:25:01.464937image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum276620
5-th percentile1962022
Q13828724
median7413553
Q316305407.5
95-th percentile45608388.7
Maximum131994091
Range131717471
Interquartile range (IQR)12476683.5

Descriptive statistics

Standard deviation14967638.22
Coefficient of variation (CV)1.134950379
Kurtosis7.087645043
Mean13187923.01
Median Absolute Deviation (MAD)4344032
Skewness2.392091872
Sum3.82317888 × 1010
Variance2.240301937 × 1014
MonotonicityNot monotonic
2021-08-06T13:25:01.561173image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
500747141
 
< 0.1%
107621051
 
< 0.1%
73672351
 
< 0.1%
101036231
 
< 0.1%
88672541
 
< 0.1%
111036611
 
< 0.1%
79320031
 
< 0.1%
82735391
 
< 0.1%
136880681
 
< 0.1%
186910101
 
< 0.1%
Other values (2889)2889
99.7%
ValueCountFrequency (%)
2766201
< 0.1%
3408111
< 0.1%
4730171
< 0.1%
6611371
< 0.1%
6862001
< 0.1%
7216631
< 0.1%
7829901
< 0.1%
8101301
< 0.1%
8656941
< 0.1%
9040051
< 0.1%
ValueCountFrequency (%)
1319940911
< 0.1%
1166922291
< 0.1%
967524891
< 0.1%
939473281
< 0.1%
938808941
< 0.1%
938124311
< 0.1%
902251071
< 0.1%
855083371
< 0.1%
852421861
< 0.1%
852214801
< 0.1%

成交金额
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct2899
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean213068441.5
Minimum1936340
Maximum4423090987
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.8 KiB
2021-08-06T13:25:01.656661image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1936340
5-th percentile13366741.51
Q133041985.98
median72939052.84
Q3213765773.8
95-th percentile882266175.3
Maximum4423090987
Range4421154647
Interquartile range (IQR)180723787.8

Descriptive statistics

Standard deviation388832994.4
Coefficient of variation (CV)1.824920629
Kurtosis28.51915276
Mean213068441.5
Median Absolute Deviation (MAD)52052901.54
Skewness4.539537797
Sum6.176854119 × 1011
Variance1.511910975 × 1017
MonotonicityNot monotonic
2021-08-06T13:25:01.744124image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20810061271
 
< 0.1%
55937295.781
 
< 0.1%
37656679.471
 
< 0.1%
52212000.51
 
< 0.1%
44514587.621
 
< 0.1%
56542552.681
 
< 0.1%
42038196.321
 
< 0.1%
43433314.361
 
< 0.1%
73674204.831
 
< 0.1%
102748748.51
 
< 0.1%
Other values (2889)2889
99.7%
ValueCountFrequency (%)
19363401
< 0.1%
2167557.961
< 0.1%
2483339.251
< 0.1%
4475824.691
< 0.1%
5180171.41
< 0.1%
52906021
< 0.1%
5652146.111
< 0.1%
5840594.471
< 0.1%
5895667.11
< 0.1%
5896539.671
< 0.1%
ValueCountFrequency (%)
44230909871
< 0.1%
42502585731
< 0.1%
40216940521
< 0.1%
37794524901
< 0.1%
33304064441
< 0.1%
33201098581
< 0.1%
32992740701
< 0.1%
32840713931
< 0.1%
30368157901
< 0.1%
27682954581
< 0.1%

总市值
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1988
Distinct (%)68.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6302179588
Minimum1542650700
Maximum3.838510377 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.8 KiB
2021-08-06T13:25:01.834341image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum1542650700
5-th percentile1997165280
Q13038377830
median4882504800
Q37967471800
95-th percentile1.528656769 × 1010
Maximum3.838510377 × 1010
Range3.684245307 × 1010
Interquartile range (IQR)4929093970

Descriptive statistics

Standard deviation4839745736
Coefficient of variation (CV)0.7679479248
Kurtosis9.441379182
Mean6302179588
Median Absolute Deviation (MAD)2079358200
Skewness2.508096678
Sum1.827001862 × 1013
Variance2.342313879 × 1019
MonotonicityNot monotonic
2021-08-06T13:25:01.927368image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19824441608
 
0.3%
34349280007
 
0.2%
57019804807
 
0.2%
44968850397
 
0.2%
57657720006
 
0.2%
20069793606
 
0.2%
19726300806
 
0.2%
20462356806
 
0.2%
19137456006
 
0.2%
19677230406
 
0.2%
Other values (1978)2834
97.8%
ValueCountFrequency (%)
15426507002
0.1%
15610521001
< 0.1%
15641190001
< 0.1%
15671859001
< 0.1%
15794535001
< 0.1%
15855873001
< 0.1%
16009218001
< 0.1%
16039887001
< 0.1%
16223901002
0.1%
16377246001
< 0.1%
ValueCountFrequency (%)
3.838510377 × 10101
< 0.1%
3.83578539 × 10101
< 0.1%
3.713308964 × 10101
< 0.1%
3.711059837 × 10101
< 0.1%
3.708732562 × 10101
< 0.1%
3.703562746 × 10101
< 0.1%
3.662924399 × 10101
< 0.1%
3.62109475 × 10101
< 0.1%
3.604601151 × 10101
< 0.1%
3.497392756 × 10101
< 0.1%

流通市值
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1952
Distinct (%)67.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5428240290
Minimum385750700
Maximum3.565454935 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.8 KiB
2021-08-06T13:25:02.023534image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum385750700
5-th percentile671175542
Q11837686480
median4288752960
Q37149557280
95-th percentile1.419914573 × 1010
Maximum3.565454935 × 1010
Range3.526879865 × 1010
Interquartile range (IQR)5311870800

Descriptive statistics

Standard deviation4834566529
Coefficient of variation (CV)0.8906323728
Kurtosis6.424589002
Mean5428240290
Median Absolute Deviation (MAD)2698860788
Skewness2.034365903
Sum1.57364686 × 1013
Variance2.337303352 × 1019
MonotonicityNot monotonic
2021-08-06T13:25:02.114662image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
412191360010
 
0.3%
39256320008
 
0.3%
19824441608
 
0.3%
54664425607
 
0.2%
57019804807
 
0.2%
20462356806
 
0.2%
57657720006
 
0.2%
19677230406
 
0.2%
20069793606
 
0.2%
19726300806
 
0.2%
Other values (1942)2829
97.6%
ValueCountFrequency (%)
3857507002
0.1%
3903521001
< 0.1%
3911190001
< 0.1%
3918859001
< 0.1%
3949535001
< 0.1%
3964873001
< 0.1%
4003218001
< 0.1%
4010887001
< 0.1%
4056901002
0.1%
4095246001
< 0.1%
ValueCountFrequency (%)
3.565454935 × 10101
< 0.1%
3.449159823 × 10101
< 0.1%
3.447070689 × 10101
< 0.1%
3.44010691 × 10101
< 0.1%
3.363505339 × 10101
< 0.1%
3.348185025 × 10101
< 0.1%
3.248602983 × 10101
< 0.1%
3.245121093 × 10101
< 0.1%
3.242335582 × 10101
< 0.1%
3.213784087 × 10101
< 0.1%

涨跌额
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct328
Distinct (%)11.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.008330458779
Minimum-4.44
Maximum4.5
Zeros69
Zeros (%)2.4%
Negative1340
Negative (%)46.2%
Memory size22.8 KiB
2021-08-06T13:25:02.208028image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-4.44
5-th percentile-0.77
Q1-0.16
median0.01
Q30.18
95-th percentile0.791
Maximum4.5
Range8.94
Interquartile range (IQR)0.34

Descriptive statistics

Standard deviation0.5451746795
Coefficient of variation (CV)65.44353606
Kurtosis11.81591228
Mean0.008330458779
Median Absolute Deviation (MAD)0.17
Skewness0.005687864129
Sum24.15
Variance0.2972154311
MonotonicityNot monotonic
2021-08-06T13:25:02.295292image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
069
 
2.4%
0.0363
 
2.2%
0.0462
 
2.1%
0.0559
 
2.0%
-0.0254
 
1.9%
-0.0354
 
1.9%
0.0652
 
1.8%
0.0152
 
1.8%
0.0851
 
1.8%
0.0950
 
1.7%
Other values (318)2333
80.5%
ValueCountFrequency (%)
-4.441
< 0.1%
-3.921
< 0.1%
-3.381
< 0.1%
-3.091
< 0.1%
-2.82
0.1%
-2.741
< 0.1%
-2.591
< 0.1%
-2.561
< 0.1%
-2.432
0.1%
-2.421
< 0.1%
ValueCountFrequency (%)
4.51
< 0.1%
4.241
< 0.1%
3.861
< 0.1%
3.271
< 0.1%
3.031
< 0.1%
2.971
< 0.1%
2.931
< 0.1%
2.721
< 0.1%
2.541
< 0.1%
2.471
< 0.1%

涨跌幅
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct2679
Distinct (%)92.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.09199244567
Minimum-10.035
Maximum10.0952
Zeros69
Zeros (%)2.4%
Negative1340
Negative (%)46.2%
Memory size22.8 KiB
2021-08-06T13:25:02.384690image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Quantile statistics

Minimum-10.035
5-th percentile-5.84227
Q1-1.56555
median0.1374
Q31.7925
95-th percentile6.00645
Maximum10.0952
Range20.1302
Interquartile range (IQR)3.35805

Descriptive statistics

Standard deviation3.44458752
Coefficient of variation (CV)37.44424333
Kurtosis1.535819714
Mean0.09199244567
Median Absolute Deviation (MAD)1.6759
Skewness0.003536367847
Sum266.6861
Variance11.86518318
MonotonicityNot monotonic
2021-08-06T13:25:02.471674image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
069
 
2.4%
1012
 
0.4%
-105
 
0.2%
2.77784
 
0.1%
-1.60433
 
0.1%
0.66233
 
0.1%
-2.94123
 
0.1%
2.05133
 
0.1%
2.18983
 
0.1%
2.22223
 
0.1%
Other values (2669)2791
96.3%
ValueCountFrequency (%)
-10.0351
< 0.1%
-10.03011
< 0.1%
-10.02952
0.1%
-10.01821
< 0.1%
-10.01811
< 0.1%
-10.01621
< 0.1%
-10.01461
< 0.1%
-10.01261
< 0.1%
-10.01241
< 0.1%
-10.0111
< 0.1%
ValueCountFrequency (%)
10.09521
< 0.1%
10.06861
< 0.1%
10.06292
0.1%
10.06061
< 0.1%
10.0521
< 0.1%
10.04881
< 0.1%
10.04131
< 0.1%
10.03791
< 0.1%
10.03721
< 0.1%
10.03661
< 0.1%

次日涨跌
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size22.8 KiB
1
1492 
0
1407 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2899
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
11492
51.5%
01407
48.5%

Length

2021-08-06T13:25:02.609742image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-08-06T13:25:02.663527image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
ValueCountFrequency (%)
11492
51.5%
01407
48.5%

Most occurring characters

ValueCountFrequency (%)
11492
51.5%
01407
48.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number2899
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
11492
51.5%
01407
48.5%

Most occurring scripts

ValueCountFrequency (%)
Common2899
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
11492
51.5%
01407
48.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII2899
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
11492
51.5%
01407
48.5%

Interactions

2021-08-06T13:24:46.428498image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:46.514155image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:46.588342image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:46.663605image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:46.736204image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:46.809229image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:46.885548image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:46.960855image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:47.036539image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:47.108677image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:47.189174image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:47.267256image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:47.340748image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:47.419914image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:47.491905image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:47.634270image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:47.701957image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:47.769485image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:47.838380image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:47.911151image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:47.984041image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:48.056586image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:48.124302image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:48.200207image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:48.274614image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:48.343275image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:48.417851image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:48.489457image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:48.560666image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:48.629657image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:48.696646image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:48.765475image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:48.831882image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:48.904500image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:48.976280image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:49.044423image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:49.118906image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:49.192551image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:49.260927image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:49.337150image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:49.411235image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:49.478818image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:49.546626image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:49.613789image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:49.686307image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:49.839088image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:49.912272image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:49.985344image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:50.053647image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:50.131414image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:50.232164image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:50.299107image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:50.373679image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:50.444622image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:50.513476image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:50.582081image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:50.650085image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:50.717124image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:50.802788image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:50.882962image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:50.954484image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:51.024138image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:51.099799image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:51.172342image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:51.248315image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:51.323731image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:51.397392image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:51.465429image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:51.536490image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:51.605827image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:51.678546image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:51.753960image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:51.831309image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:51.906680image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:51.975588image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:52.050881image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:52.128298image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:52.196138image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:52.272727image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:52.350206image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:52.422101image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:52.597832image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:52.671937image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:52.745451image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:52.821159image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:52.895730image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:52.971840image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:53.044863image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:53.123561image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:53.202773image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:53.273893image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:53.353236image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:53.431921image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:53.507374image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:53.581689image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:53.656040image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:53.734627image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:53.809534image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:53.890531image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:53.970224image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:54.044050image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:54.124536image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:54.204640image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:54.278491image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:54.359289image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:54.432804image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:54.501578image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:54.570928image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:54.638169image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:54.705797image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:54.777693image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:54.855145image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:54.929947image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:54.998068image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:55.074277image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:55.150313image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:55.218038image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:55.294984image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:55.378858image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:55.456377image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:55.533939image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:55.611717image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:55.690256image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:55.771244image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:55.981168image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:56.063454image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:56.142929image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:56.230613image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:56.315169image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:56.393416image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:56.477071image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:56.556960image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:56.633958image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:56.711255image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:56.786599image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:56.862090image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:56.939054image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:57.017879image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:57.098114image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:57.173577image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:57.256949image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:57.342437image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:57.416928image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:57.505360image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:57.577114image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:57.643661image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:57.710696image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:57.777775image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:57.846797image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:57.916194image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:57.985590image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:58.059347image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:58.127084image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:58.200465image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:58.274590image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:58.350040image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:58.430967image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:58.514444image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:58.592030image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:58.670025image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:58.746169image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:58.822837image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:58.900090image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:58.981285image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:59.062031image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:59.142043image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:59.226651image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:59.311050image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
2021-08-06T13:24:59.393414image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Correlations

2021-08-06T13:25:02.721047image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-08-06T13:25:02.841006image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-08-06T13:25:02.959685image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-08-06T13:25:03.078315image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-08-06T13:24:59.588440image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
A simple visualization of nullity by column.
2021-08-06T13:24:59.757417image/svg+xmlMatplotlib v3.4.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_index日期收盘价最高价最低价开盘价前收盘换手率成交量成交金额总市值流通市值涨跌额涨跌幅次日涨跌
002021/8/542.3842.8240.1441.0642.917.1907500747142.081006e+09366292439932.951250e+10-0.53-1.23510
112021/8/442.9143.3940.1840.6239.949.2611644923182.693173e+09370873256192.988158e+102.977.43620
222021/8/339.9443.2439.9443.0044.389.1361636215512.616777e+09345203399032.781333e+10-4.44-10.00451
332021/8/244.3849.7343.4749.0048.3013.8937967524894.423091e+09383578539033.090525e+10-3.92-8.11590
442021/7/3048.3051.8945.7047.5648.089.8992689360863.320110e+09362109475013.363505e+100.220.45760
552021/7/2948.0849.9846.6148.5046.567.7063536652772.607217e+09360460115093.348185e+101.523.26461
662021/7/2846.5648.1843.2045.5446.156.5286454640362.112774e+09349064537413.242336e+100.410.88841
772021/7/2746.1553.1345.0049.1049.538.0903563390442.768295e+09345990730273.213784e+10-3.38-6.82411
882021/7/2649.5350.6647.3049.0149.405.3599373254271.834160e+09371330896433.449160e+100.130.26320
992021/7/2349.4052.7047.9250.0051.206.9568484457822.429679e+09370356274653.440107e+10-1.80-3.51561

Last rows

df_index日期收盘价最高价最低价开盘价前收盘换手率成交量成交金额总市值流通市值涨跌额涨跌幅次日涨跌
288932032008/6/617.0117.4716.8016.9116.856.491039823316.844929e+0752167969001.043598e+090.160.94960
289032042008/6/516.8516.9816.6716.9516.864.888629992595.039372e+0751677265001.033781e+09-0.01-0.05931
289132052008/6/416.8617.1816.7617.1817.055.137331518645.317705e+0751707934001.034395e+09-0.19-1.11440
289232062008/6/317.0517.4716.9517.3017.277.741947498058.162274e+0752290645001.046052e+09-0.22-1.27390
289332072008/6/217.2717.4817.0117.2517.246.321138781046.687937e+0752965363001.059549e+090.030.17400
289432082008/5/3017.2417.7516.8317.6817.7011.279369200841.187726e+0852873356001.057708e+09-0.46-2.59891
289532092008/5/2917.7018.1717.5117.6417.6411.183468612551.224930e+0854284130001.085930e+090.060.34010
289632102008/5/2817.6418.0917.3017.8418.0116.081998665431.742992e+0854100116001.082249e+09-0.37-2.05441
289732112008/5/2718.0118.8017.8518.6818.4015.391794431111.725063e+0855234869001.104950e+09-0.39-2.11960
289832122008/5/2618.4019.6918.4019.6920.4429.0461178203703.315659e+0856430960001.128877e+09-2.04-9.98040